Survival Analysis Using SAS®: A Practical Guide

Table of contents

1. Copyright
2. Acknowledgments
3. Introduction
   1. What Is Survival Analysis?
   2. What Is Survival Data?
   3. Why Use Survival Analysis?
   4. Approaches to Survival Analysis
   5. What You Need to Know
   6. Computing Notes
4. Basic Concepts of Survival Analysis
   1. Introduction
   2. Censoring
   3. Describing Survival Distributions
   4. Interpretations of the Hazard Function
   5. Some Simple Hazard Models
   6. The Origin of Time
   7. Data Structure
5. Estimating and Comparing Survival Curves with PROC LIFETEST
   1. Introduction
   2. The Kaplan-Meier Method
   3. Testing for Differences in Survivor Functions
   4. The Life-Table Method
   5. Life Tables from Grouped Data
   6. Testing for Effects of Covariates
   7. Log Survival and Smoothed Hazard Plots
   8. Conclusion
6. Estimating Parametric Regression Models with PROC LIFEREG
   1. Introduction
   2. The Accelerated Failure Time Model
   3. Alternative Distributions
   4. Categorical Variables and the CLASS Statement
   5. Maximum Likelihood Estimation
   6. Hypothesis Tests
   7. Goodness-of-Fit Tests with the Likelihood-Ratio Statistic
   8. Graphical Methods for Evaluating Model Fit
   9. Left Censoring and Interval Censoring
   10. Generating Predictions and Hazard Functions
   11. The Piecewise Exponential Model
   12. Conclusion
7. Estimating Cox Regression Models with PROC PHREG
   1. Introduction
   2. The Proportional Hazards Model
   3. Partial Likelihood
   4. Tied Data
   5. Time-Dependent Covariates
   6. Cox Models with Nonproportional Hazards
   7. Left Truncation and Late Entry into the Risk Set
   8. Estimating Survivor Functions
   9. Residuals and Influence Statistics
   10. Testing Linear Hypotheses with the TEST Statement
   11. Conclusion
8. Competing Risks
   1. Introduction
   2. Type-Specific Hazards
   3. Time in Power for Leaders of Countries: Example
   4. Estimates and Tests Without Covariates
   5. Covariate Effects Via Cox Models
   6. Accelerated Failure Time Models
   7. An Alternative Approach to Multiple Event Types
   8. Conclusion
9. Analysis of Tied or Discrete Data with the LOGISTIC, PROBIT, and GENMOD Procedures
   1. Introduction
   2. The Logit Model for Discrete Time
   3. The Complementary Log-Log Model for Continuous-Time Processes
   4. Data with Time-Dependent Covariates
   5. Issues and Extensions
   6. Conclusion
10. Heterogeneity, Repeated Events, and Other Topics
    1. Introduction
    2. Unobserved Heterogeneity
    3. Repeated Events
    4. Generalized R2
    5. Sensitivity Analysis for Informative Censoring
11. A Guide for the Perplexed
    1. How to Choose a Method
    2. Conclusion
12. Macro Programs
    1. Introduction
    2. The SMOOTH Macro
    3. The LIFEHAZ Macro
    4. The WLW Macro
13. Data Sets
    1. Introduction
    2. The MYEL Data Set: Myelomatosis Patients
    3. The RECID Data Set: Arrest Times for Released Prisoners
    4. The STAN Data Set: Stanford Heart Transplant Patients
    5. The BREAST Data Set: Survival Data for Breast Cancer Patients
    6. The JOBDUR Data Set: Durations of Jobs
    7. The ALCO Data Set: Survival of Cirrhosis Patients
    8. The LEADERS Data Set: Time in Power for Leaders of Countries
    9. The RANK Data Set: Promotions in Rank for Biochemists
    10. The JOBMULT Data Set: Repeated Job Changes
14. References
15. Books Available from SAS Press
    1. JMP® Books
16. Index